使生成式人工智能与人类偏好对齐：面向在线评论管理的新型大语言模型微调方法
发布时间：2026-06-26
6月
24
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时间和日期
|
2026-06-24 (星期三) 10:30 上午
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12:00 下午
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| 标题 | 使生成式人工智能与人类偏好对齐：面向在线评论管理的新型大语言模型微调方法 |
| 日期和时间 |
2026年6月24日（周三） 10:30 - 12:00 |
| 地点 | 综合教学楼D604会议室 |
| 主讲人 |
葛永 |
| 摘要 | Online reviews have played a pivotal role in consumers’ decision-making processes. Existing research has highlighted the significant impact of managerial review responses on customer relationship management and firm performance. However, large portions of online reviews remain unaddressed because of the considerable human labor required to respond to the rapid growth of online reviews. Although generative artificial intelligence (AI; especially large language models (LLMs)) has achieved remarkable success in a range of tasks, they (i.e., generative AI, such as GPT-4) are general-purpose models and may not align well with domain-specific human preferences. To tailor these general generative AI models to domain-specific applications, fine-tuning is commonly employed. Nevertheless, several challenges persist in fine-tuning with domain-specific data, including hallucinations, difficulty in representing domain-specific human preferences, and overconservatism in offline policy optimization. To address these challenges, we propose a novel preference fine-tuning method to align an LLM with domain-specific human preferences for generating online review responses. Specifically, we first identify the source of hallucination and propose an effective context-augmentation approach to mitigate the LLM hallucination. To represent human preferences, we propose a novel theory-driven preference fine-tuning approach that automatically constructs human preference pairs in the online review domain. Additionally, we propose a curriculum learning approach to further enhance preference fine-tuning. To overcome the challenge of overconservatism in the existing offline preference fine-tuning method, we propose a novel density estimation-based support-constraint method to relax the conservatism, and we mathematically prove its superior theoretical guarantees. Extensive evaluations employing objective evaluation metrics, human assessments, and qualitative analyses substantiate the superiority of our proposed preference fine-tuning method. For practical deployment in real-world systems, we recommend two types of hybrid approaches to synergize human and LLM capabilities, which can significantly reduce human labor and time in responding to online reviews. |
| 主讲人简介 |
葛永博士是亚利桑那大学埃勒管理学院管理信息系统系教授。他于2013年在新泽西州立罗格斯大学商学院获得信息技术博士学位。他的主要研究领域包括数据挖掘、大数据、机器学习与深度学习、推荐系统、个性化服务、社交网络、目标营销、人才分析以及商业分析。他在计算机科学和管理信息系统领域的期刊和会议上发表论文100余篇，其中包括IEEE Transactions on Knowledge and Data Engineering、ACM SIGKDD International Conference on Knowledge Discovery and Data Mining以及MIS Quarterly。他的研究得到了美国国家科学基金会（NSF）和美国国立卫生研究院（NIH）的广泛资助。他在埃勒管理学院教授多门课程，包括《深度学习导论》、《生成式人工智能在商业中的应用》、《数据分析》、《大数据技术》、《商业智能》以及《设计科学》。他于2019年获得美国国家科学基金会职业奖（NSF CAREER Award）。目前担任MIS Quarterly副主编。 |
